Traditional GPU programming with CUDA requires developers to think about threads, warps, and memory hierarchies. While powerful, this approach requires the programmer to map algorithms onto hardware efficiently. With CUDA Tile, developers describe operations on tiles of data, and the compiler handles the mapping to hardware. Consider vector addition. In the traditional GPU programming model, using CUDA.jl, the programmer must manage individual threads explicitly: using CUDA function vadd(a, b, c, n) i = (blockIdx().x - 1) * blockDim().x + threadIdx().x if i <= n @inbounds c[i] = a[i] + b[i
cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia | NVIDIA Technical Blog… What is tile-based GPU programming? Traditional GPU programming with CUDA requires developers to think about threads, warps, and memory hierarchies. While powerful, this approach requires the programmer to map algorithms onto hardware efficiently. …
… By making data movement explicit and programmable, the LPU enables memory access, compute, and communication to be overlapped, rather than relying on hardware heuristics. …